Dynamic intelligent objects

ABSTRACT

An contextual artificial intelligence system is disclosed. Intelligent business objects enable dynamic data object interaction and encapsulation of user context. Data is rationalized and data objects evolve by way of an artificial intelligence assisted process of self-discovery. Significant data is identified based upon factors such as cost, revenue and outcome and contextually significant result sets are automatically generated for users.

FIELD OF INVENTION

The present invention generally relates to applying artificialintelligence to an analysis of business data, and more particularly, tosystems and methods for enabling users to view enterprise resource dataand relationships from the user's unique contextual perspective.

BACKGROUND OF THE INVENTION

The dynamic nature of information often makes it difficult and costly tomaintain, and typically due to constant change, the data intrinsicallylacks complete accuracy. Most systems require absolute data, which isoften difficult to obtain and misleading because it lacks absoluteaccuracy. In addition, the various systems of an organization typicallycontain “application firewalls” between the systems. Each of thesedisparate systems is geared toward different user constituencies orbusiness functions, and although much of the data originates from thesame source, the disparate systems effectively contribute to datacorruption. Furthermore, systems are usually architected to limit thedata from self-learning. Fixed relationships pre-define context, andsuch relationships do not respond well to divergent user needs. Suchdata compartmentalization often undermines organizational performanceby, for example, limiting insight into the impacts of decision-makingacross all planes of significance (e.g., cost, revenue, customeroutcome) and contributing little to the understanding of how thedownstream impact of such decisions greatly affects the bottom line.

Therefore, a need exists for a system and method for processinginformation based upon probabilistic significance that varies with avariety of factors such as time, user, industry, external events and thevalue of the data itself. In order to promote sophisticatedunderstanding of information and its impact on a given organization, aneed exists for a system that combines the use of sophisticated objectdata models, probabilistic modeling, statistical analysis and artificialintelligence techniques to create intelligent, self-learning dataobjects, and to derive relevant and insightful results for a multitudeof end-user constituencies.

SUMMARY OF THE INVENTION

The system includes an Understanding Generator (“UG”) softwareapplication which comprises, in one embodiment a data rationalizer, anintelligent business object creator, a context profiler, a memorycreator, a context overlay generator, a relationship generator, awhat-if analyzer, a trend analyzer, a corrective action analyzer, aresults formatter and an object database. The UG creates businessobjects that are self-aware and self-learning, and that enable thesystem to generate relevant results, without the need to be prompted bya user request. UG yields useful result sets by contextualizing answersvia relevant significance, which are then, in one embodiment,prioritized by the user against other result sets obtained across alldimensions and all interested parties.

UG may model user perception as an energy field that is experienced overtime. UG sequences and groups these perceptions into knowledge, and suchknowledge is discerned by context. Experience of the context can yieldsignificance and such significance is expressed as a probability.Business objects are therefore selected for a result set because of theprobability of the business objects' significance to the businessproblem as perceived by the user. Such selection contributes to thecreation of understanding expressed as the perception of significantcontext. Hence, in UG, understanding is the practical application ofknowledge.

More particularly, the invention uses artificial intelligence tounderstand user context and the contextual significance of data in orderto generate accurate, tailored and predictive result sets. In oneembodiment, the invention includes: i) an artificial intelligencecontext profiler that is configured to create a proxy of the user in thesystem and interpret job function, personality profile and preference tocreate a user context and encapsulate it in a user context intelligentcontext object; ii) an artificial intelligence data rationalizerconfigured to processes and interrelates input data into self-learningdata objects including intelligent physical object that are configuredto store static attributes of a data element, intelligent energy objectsthat are configured to store the dynamic attributes of the data elementand intelligent context objects that are configured to store dataelement context; and iii) a probability of fitness function that usesartificial intelligence, probabilistic and forecasting techniques todiscern the significance of data within a given context.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present system and method may bederived by referring to the detailed description and claims whenconsidered in connection with the Figures, wherein like referencenumbers refer to similar elements throughout the Figures, and:

FIG. 1 is an overview of a representative system for providing a dataanalysis and relationships in the user's unique context, in accordancewith one embodiment of the present invention.

FIG. 2 is a conceptual overview of the major functions of the system, inaccordance with one embodiment of the present invention.

FIG. 3 is a diagram of broad process flow overview, in accordance withone embodiment of the present invention.

FIG. 4 is a representative process flow diagram for instantiating anobject, in accordance with one embodiment of the present invention.

FIG. 5 is a representative process flow showing object interactionwithin the system to dynamically create hierarchies and relationships,in accordance with one embodiment of the present invention.

FIG. 6 is a data awareness ladder illustrating how data evolves, inaccordance with one embodiment of the present invention.

DETAILED DESCRIPTION

The detailed description of exemplary embodiments of the inventionherein makes reference to the accompanying figures, which show theexemplary embodiment for purposes of illustration and its best mode, andnot of limitation. While these representative embodiments are describedin sufficient detail to enable those skilled in the art to practice theinvention, it should be understood that other embodiments may berealized and that logical, software, hardware and mechanical changes maybe made without departing from the spirit and scope of the invention.For example, the steps recited in any of the method or processdescriptions may be executed in any order and are not limited to theorder presented. References to singular include plural, and referencesto plural include singular.

For the sake of brevity, conventional data networking, applicationdevelopment and other functional aspects of the systems (and componentsof the individual operating components of the systems) may not bedescribed in detail herein. Furthermore, the connecting lines shown inthe various figures contained herein are intended to represent exemplaryfunctional relationships and/or physical couplings between the variouselements. It should be noted that many alternative or additionalfunctional relationships or physical connections may be present in apractical system.

In one embodiment, the system and method is implemented in thehealthcare industry to enhance organizational performance in managingits supply chain. While described herein in reference to enhancingsupply chain information management in the healthcare industry,practitioners will appreciate that the invention may further beimplemented to provide intelligent, contextually significant datamanagement in any industry (e.g. consumer electronics, automotive,transportation, etc.).

While the description makes reference to specific technologies, systemarchitectures and data management techniques, practitioners willappreciate that the embodiments disclosed herein are examples and thatother devices and/or methods may be implemented without departing fromthe scope of the invention. Similarly, while the description makesfrequent reference to a web client, practitioners will appreciate thatother examples of user interfaces that submit messaging requests may beaccomplished by using a variety of user interfaces including handhelddevices such as personal digital assistants and cellular telephones.Practitioners will also appreciate that a web client is but oneembodiment and that other communication, data transmission andnetworking devices and/or methods may be implemented without departingfrom the scope of the invention.

While the system may contemplate upgrades or reconfigurations ofexisting processing systems, changes to existing databases and businessinformation system tools are not necessarily required by the presentinvention. For example, the present system may contemplate, but does notrequire the use of a commercially available off-the-shelf objectoriented database management system. Moreover, the system may beseamlessly integrated into existing information technology, datamanagement architectures, enterprise resource planning and/or businessinformation system tools with minimal or no changes to existing systems.

Exemplary benefits provided by this invention include cost savings fromreduced errors, lower costs from better contract alignment, and improvedrevenues through more accurate, continuously conditioned data. Creatingthe necessary linkage among most or all of the data elements (i.e.content) in an organization's supply chain allows the organization tocontrol spending, increase contract compliance and improve receivablesperformance. The system provides access to continuously current andaccurate information, provides individual users with customizedinformation relevant to their unique context, and maintains multiplecontextual understandings and output descriptions for any product toassist in delivering useful data to the right people in theorganization.

In one embodiment, the invention is implemented to enhance the data andorganizational understanding of the data in the healthcare industry. Thesystem enables multiple hospitals, their suppliers, vendors and otherconstituents to, for example, (i) upload data such as the vendor masterscheme, item master schema and purchase order history files in onesimple step; (ii) produce content assessment reports and create acontent management strategy for the organization. The assessment reportsenable the organization to: determine how often items are used anddetermine items for removal (data relevance analysis); identify productdescription improvement opportunities and reconcile potentiallyduplicate items (data consistency analysis); and, display all or anyportion of the data uploaded from a Materials Management InformationSystem (MMIS) to determine if any data is missing (data completenessanalysis); (iii) analyze supply chain costs to support and drive contentmanagement priorities; and/or (iv) optimize content managementfunctions.

With reference to FIG. 1, the representative system includes a user 105interfacing with an enterprise application computing environment(“EACE”) 115 by way of web client 110. A user is any individual, entity,business, organization, third-party entity, software and/or hardwarethat interfaces with EACE 115 to access applications or data. User 105may interface with Internet server 125 via any communication protocol,device or method discussed herein, known in the art, or later developed.In one embodiment, user 105 may interact with the EACE 115 via anInternet browser at web client 110.

Transmissions between user 105 and the Internet server 125 may passthrough a firewall 120 to help ensure the integrity of the EACE 115components. Practitioners will appreciate that the invention mayincorporate any number of security schemes or none at all. In oneembodiment, the Internet server 125 receives page requests from the webclient 110 and interacts with various other system 100 components toperform tasks related to requests from the web client 110. Internetserver 125 may invoke an authentication server 130 to verify theidentity of user 105 and assign specific access rights to user 105.Authentication database 135 may store information used in theauthentication process such as, for example, user identifiers,passwords, access privileges, user preferences, user statistics, and thelike. When a request to access EACE 115 is received from Internet server125, EACE 115 determines if authentication is required and transmits aprompt to the web client 110. User 105 enters authentication data at theweb client 110, which transmits the authentication data to Internetserver 125. Internet server 125 passes the authentication data toauthentication server which queries the user database 140 forcorresponding credentials. When user 105 is authenticated, user 105 mayaccess various applications and their corresponding data sources.

When user 105 logs on to an application, Internet server 125 may invokean application server 145. Application server 145 invokes logic in userinterface application (“UIA”) 147 by passing parameters relating to theuser's 105 requests for data. UIA 147 may include any hardware and/orsoftware suitably configured to receive requests from the web client 110(e.g., via Internet server 125 and the application server 145). UIA 147may communicate with (e.g. submit requests and receive responses)understanding generator (UG) 165, Understanding Generator ObjectDatabase (“UGOD”) 190 or any other system 100 component. UIA 147 isfurther configured to process requests, construct database queries,and/or execute queries against databases, external data sources andtemporary databases, as well as exchange data with other applicationmodules (not pictured). In one embodiment, UIA 147 may be configured tointeract with other system 100 components to perform complexcalculations, retrieve additional data, format data into reports, createXML representations of data, construct markup language documents, and/orthe like. Moreover, the UIA 147 may partially or fully reside as astandalone system or may be incorporated with the application server 145or any other EACE 115 component as program code.

As discussed in further detail in the process descriptions of FIGS. 3-5,EACE 115 components (e.g. UIA 147) may interface with the UnderstandingGenerator (UG) 165 and its associated components. In one embodiment, UG165 is a software application that includes multiple software modulessuch as, for example: IBO data rationalizer 166, IBO creator 167,context profiler 168, memory creator 169, context overlays generator170, relationship generator 171, what-if analyzer 172, trend analyzer173, results formatter 174, corrective action analyzer 175 and contextsearch engine 176.

As practitioners will appreciate, while depicted as a single entity forthe purposes of illustration, exemplary databases depicted in FIG. 1 mayrepresent multiple physical and/or hardware, software, database, datastructure and networking components. FIG. 1 depicts the types ofdatabases that are included in an exemplary embodiment which includeUGOD 190, EDMS 150, user database 140, authorization database 135, andexternal data source 161. As practitioners will appreciate, theillustrated databases may include a physical coupling of multiple datasources or represent a logical relationship. Therefore, embodiments mayinclude all or none of the components illustrated in FIG. 1, withoutdeparting from the scope of the invention. For instance, in oneembodiment, raw transactional data that is logically associated withUGOD 170 is stored in a relational database component physicallyseparate from UGOD 170.

User database 140 may store user profile and credential information andauthentication server 135 may store security settings and otherassociated data (e.g. encryption keys).

UG 165 (and its associated software modules) may interface with variousdata sources including UGOD 190 and enterprise data management system(EDMS) 150. UGOD 190 stores the objects, associations, data andrelationships of UG 165 in intelligent business objects (IBOs). IBOs mayhave core elements such as attributes (ID, Description), dynamicattributes (“object energy”), context, relationships and planes ofawareness. Types of IBOs stored in UGOD 190 include, for example,subject/objects (S/Os), Intelligent energy objects (IEOs), Intelligentcontent objects (ICOs), Intelligent spatial objects (ISOs), andIntelligent Memory Objects (IMOs).

S/Os are the core intelligent business object around which all the otherobjects are built and associated i.e. they represent an independententity that owns other object components. S/Os contain base data fields.Each S/O is encapsulated with an intrinsic purpose and understanding. Inone embodiment, the base data fields include IDs, descriptions,relationships, attributes and plane of significance (e.g. cost, revenueand outcome).

IEOs are dynamic data fields associated with one or more S/Os where theS/O's dynamic attributes (e.g. price, volume, margin, etc.) areprocessed based upon constantly changing data.

ICOs are owned by the S/Os and are used to define the applied contextualparameters such as relationships, IEO value parameters, significance,and other aspects of context.

ISOs define timeframes.

An IMO is created as a result of an interaction between an S/O and someother object (S/O, IEO, IBO, etc). During this interaction an IMO iscreated provided the encounter is considered significant to thesubjective nature of the S/O.

EDMS 150 stores and/or provides access to data feeds and other datasources depicted in FIG. 1 such as data source1 151, data source2 152and data sourceN 153. As practitioners will appreciate, certainembodiments may access data from any external data source that providesuseful and accurate data. For instance the EACE 115, EDMS 150 or anyother system 100 component may be interconnected to an external datasource 161 via a second network, referred to as the external gateway163. The external gateway 163 may include any hardware and/or softwaresuitably configured to facilitate communications and/or processtransactions between the EACE 115 and the external data source 161.Interconnection gateways are commercially available and known in theart. External gateway 163 may be implemented through commerciallyavailable hardware and/or software, through custom hardware and/orsoftware components, or through a combination thereof. External gateway163 may reside in a variety of configurations and may exist as astandalone system or may be a software component residing either insidesystem 100, the external data source 161 or any other knownconfiguration. External gateway 163 may be configured to deliver datadirectly to system 100 components (e.g., UIA 147) and to interact withother systems and components (e.g., EACE 115, EDMS 150) or any othersystem 100. In one embodiment, the external gateway 163 may comprise webservices that are invoked to exchange data between the various disclosedsystems. The external gateway 163 represents existing proprietarynetworks that presently accommodate data exchange for data such asfinancial transactions, customer demographics, billing transactions andthe like. The external gateway 163 may be a closed network that isassumed to be secure from eavesdroppers.

As practitioners will appreciate, embodiments are not limited to theexemplary databases described above, nor do embodiments necessarilyutilize each of the disclosed exemplary databases. In addition to thecomponents described above, the system 100, the UGOD 170 and the EACE115 may further include one or more of the following: a host server orother computing systems including a processor for processing digitaldata; a memory coupled to the processor for storing digital data; aninput digitizer coupled to the processor for inputting digital data; anapplication program stored in the memory and accessible by the processorfor directing processing of digital data by the processor; a displaydevice coupled to the processor and memory for displaying informationderived from digital data processed by the processor; and a plurality ofdatabases.

As will be appreciated by one of ordinary skill in the art, one or moresystem 100 components may be embodied as a customization of an existingsystem, an add-on product, upgraded software, a stand-alone system(e.g., kiosk), a distributed system, a method, a data processing system,a device for data processing, and/or a computer program product.Accordingly, individual system 100 components may take the form of anentirely software embodiment, an entirely hardware embodiment, or anembodiment combining aspects of both software and hardware. Furthermore,individual system 100 components may take the form of a computer programproduct on a computer-readable storage medium having computer-readableprogram code means embodied in the storage medium. Any suitablecomputer-readable storage medium may be utilized, including hard disks,CD-ROM, optical storage devices, magnetic storage devices, and/or thelike.

The systems and methods contemplate uses in association with thehealthcare industry, supply management systems, content managementsystems, materials management systems, accounting systems, artificialintelligence systems, expert systems, accounts receivable systems,operational management systems, cash management tools, logisticalplanning tools, business intelligence systems, reporting systems, webservices, pervasive and individualized solutions, open source,biometrics, mobility and wireless solutions, commodity computing, gridcomputing and/or mesh computing. For example, in an embodiment, the webclient 110 is configured with a biometric security system that may beused for providing biometrics as a secondary form of identification. Thebiometric security system may include a transaction device and a readercommunicating with the system. The biometric security system also mayinclude a biometric sensor that detects biometric samples and a devicefor verifying biometric samples. The biometric security system may beconfigured with one or more biometric scanners, processors and/orsystems. A biometric system may include one or more technologies, or anyportion thereof, such as, for example, recognition of a biometric. Asused herein, a biometric may include a user's voice, fingerprint,facial, ear, signature, vascular patterns, DNA sampling, hand geometry,sound, olfactory, keystroke/typing, iris, retinal or any other biometricrelating to recognition based upon any body part, function, system,attribute and/or other characteristic, or any portion thereof.

Web client 110 comprises any hardware and/or software suitablyconfigured to facilitate requesting, retrieving, updating, analyzing,entering or modifying data such as marketing data or any informationdiscussed herein. Web client 110 includes any device (e.g., personalcomputer), which communicates (in any manner discussed herein) with theEACE 115 via any network discussed herein. Such browser applicationscomprise Internet browsing software installed within a computing unit orsystem to conduct online transactions and communications. Thesecomputing units or systems may take the form of a computer or set ofcomputers, although other types of computing units or systems may beused, including laptops, notebooks, hand-held computers, set-top boxes,workstations, computer-servers, mainframe computers, mini-computers, PCservers, pervasive computers, network sets of computers, and/or thelike. Practitioners will appreciate that the web client 110 may or maynot be in direct contact with the EACE 115. For example, the web client110 may access the services of the EACE 115 through another server,which may have a direct or indirect connection to Internet server 125.

As those skilled in the art will appreciate, the web client 110 includesan operating system (e.g., Windows NT, 95/98/2000, OS2, UNIX, Linux,Solaris, MacOS, etc.) as well as various conventional support softwareand drivers typically associated with computers. Web client 110 mayinclude any suitable personal computer, network computer, workstation,mini-computer, mainframe, mobile device or the like. Web client 110 canbe in a home or business environment with access to a network. In anembodiment, access is through a network or the Internet through acommercially available web-browser software package.

Web client 110 may be independently, separately or collectively suitablycoupled to the network via data links which includes, for example, aconnection to an Internet Service Provider (ISP) over the local loop asis typically used in connection with standard modem communication, cablemodem, Dish networks, ISDN, Digital Subscriber Line (DSL), or variouswireless communication methods, see, e.g., Gilbert Held, UnderstandingData Communications (1996), which is hereby incorporated by reference.It is noted that the network may be implemented as other types ofnetworks, such as an interactive television (ITV) network.

Firewall 120, as used herein, may comprise any hardware and/or softwaresuitably configured to protect the EACE 115 components from users ofother networks. Firewall 120 may reside in varying configurationsincluding stateful inspection, proxy-based and packet filtering, amongothers. Firewall 120 may be integrated as software within Internetserver 125, any other system components, or may reside within anothercomputing device or may take the form of a standalone hardwarecomponent.

Internet server 125 may include any hardware and/or software suitablyconfigured to facilitate communication between the web client 110 andone or more EACE 115 components. Further, Internet server 125 may beconfigured to transmit data to the web client 110 within markup languagedocuments. As used herein, “data” may include encompassing informationsuch as commands, queries, files, data for storage, and/or the like indigital or any other form. Internet server 125 may operate as a singleentity in a single geographic location or as separate computingcomponents located together or in separate geographic locations.

Internet server 125 may provide a suitable web site or otherInternet-based graphical user interface, which is accessible by users.An Internet server may provide a suitable web site or otherInternet-based graphical user interface which is accessible by users. Inone embodiment, the Microsoft Internet Information Server (IIS),Microsoft Transaction Server (MTS), and Microsoft SQL Server, are usedin conjunction with the Microsoft operating system, Microsoft NT webserver software, a Microsoft SQL Server database system, and a MicrosoftCommerce Server. Additionally, components such as Access or MicrosoftSQL Server, Oracle, Sybase, Informix MySQL, InterBase, etc., may be usedto provide an Active Data Object (ADO) compliant database managementsystem. In one embodiment, the Apache web server is used in conjunctionwith a Linux operating system, a MySQL database, and/or the Perl, PHP,and Python programming languages.

Any of the communications, inputs, storage, databases or displaysdiscussed herein may be facilitated through a web site having web pages.The term “web page” as it is used herein is not meant to limit the typeof documents and applications that may be used to interact with theuser. For example, a typical web site may include, in addition tostandard HTML documents, various forms, Java applets, JavaScript, activeserver pages (ASP), Microsoft .NET Framework, common gateway interfacescripts (CGI), extensible markup language (XML), dynamic HTML, AJAX(Asynchronous Javascript And XML), cascading style sheets (CSS), helperapplications, plug-ins, and/or the like. A server may include a webservice that receives a request from a web server, the request includinga URL (http://yahoo.com/stockquotes/ge) and an IP address (123.56.789).The web server retrieves the appropriate web pages and sends the data orapplications for the web pages to the IP address. Web services areapplications that are capable of interacting with other applicationsover a communications means, such as the Internet. Web services aretypically based on standards or protocols such as XML, S/OAP, AJAX, WSDLand UDDI. Web services methods are well known in the art, and arecovered in many standard texts. See, e.g., Alex Nghiem, IT Web Services:A Roadmap For The Enterprise (2003) Or Web Services Architecture, W3CWorking Group Note 11 Feb. 2004, available athttp://www.w3.org/TR/2004/NOTE-ws-arch-20040211, both of which arehereby incorporated by reference.

Application server 145 may include any hardware and/or software suitablyconfigured to serve applications and data to a connected web client 110.Like Internet server 125, the application server 145 may communicatewith any number of other servers, databases and/or components throughany means known in the art. Further, the application server 145 mayserve as a conduit between the web client 110 and the various systemsand components of the EACE 115. Internet server 125 may interface withthe application server 145 through any means known in the art includinga LAN/WAN, for example. Application server 145 may further invokesoftware modules such as the UIA 147 in response to user 105 requests.

In order to control access to the application server 145 or any othercomponent of the EACE 115, Internet server 125 may invoke anauthentication server 130 in response to user 105 submissions ofauthentication credentials received at Internet server 125.Authentication server 130 may include any hardware and/or softwaresuitably configured to receive authentication credentials, encrypt anddecrypt credentials, authenticate credentials, and/or grant accessrights according to pre-defined privileges attached to the credentials.Authentication server 130 may grant varying degrees of application anddata level access to users based on information stored within the userdatabase 140.

In one embodiment, the system 100 may employ middleware which mayinclude any hardware and/or software suitably configured to facilitatecommunications and/or process transactions between disparate computingsystems. Middleware components are commercially available and known inthe art. Middleware may be implemented through commercially availablehardware and/or software, through custom hardware and/or softwarecomponents, or through a combination thereof. Middleware may reside in avariety of configurations and may exist as a standalone system or may bea software component residing on the Internet server. Middleware may beconfigured to process transactions between the various components of anapplication server and any number of internal or external systems forany of the purposes disclosed herein. WebSphere MQ™ (formerly MQSeries)by IBM, Inc. (Armonk, N.Y.) is an example of a commercially availablemiddleware product. An Enterprise Service Bus (“ESB”) application isanother example of middleware.

Practitioners will appreciate that an element or database may beconfigured to store a specified type or value of data and/or the elementor database may actually store such data. Any databases discussed hereinmay include relational, hierarchical, graphical, or object-orientedstructure and/or any other database configurations. Common databaseproducts that may be used to implement the databases include DB2 by IBM(Armonk, N.Y.), various database products available from OracleCorporation (Redwood Shores, Calif.), Microsoft Access or Microsoft SQLServer by Microsoft Corporation (Redmond, Wash.), MySQL by MySQL AB(Uppsala, Sweden), or any other suitable database product. Moreover, thedatabases may be organized in any suitable manner, for example, as datatables or lookup tables. Each record may be a single file, a series offiles, a linked series of data fields or any other data structure.Association of certain data may be accomplished through any desired dataassociation technique such as those known or practiced in the art. Forexample, the association may be accomplished either manually orautomatically. Automatic association techniques may include, forexample, a database search, a database merge, GREP, AGREP, SQL, using akey field in the tables to speed searches, sequential searches throughall the tables and files, sorting records in the file according to aknown order to simplify lookup, and/or the like. The association stepmay be accomplished by a database merge function, for example, using a“key field” in pre-selected databases or data sectors. Various databasetuning steps are contemplated to optimize database performance. Forexample, frequently used files such as indexes may be placed on separatefile systems to reduce In/Out (“I/O”) bottlenecks.

More particularly, a “key field” partitions the database according tothe high-level class of objects defined by the key field. For example,certain types of data may be designated as a key field in a plurality ofrelated data tables and the data tables may then be linked on the basisof the type of data in the key field. The data corresponding to the keyfield in each of the linked data tables is preferably the same or of thesame type. However, data tables having similar, though not identical,data in the key fields may also be linked by using AGREP, for example.In accordance with one embodiment, any suitable data storage techniquemay be utilized to store data without a standard format. Data sets maybe stored using any suitable technique, including, for example, storingindividual files using an IS/O/IEC 7816-4 file structure; implementing adomain whereby a dedicated file is selected that exposes one or moreelementary files containing one or more data sets; using data setsstored in individual files using a hierarchical filing system; data setsstored as records in a single file (including compression, SQLaccessible, hashed via one or more keys, numeric, alphabetical by firsttuple, etc.); Binary Large Object (BLOB); stored as ungrouped dataelements encoded using IS/O/IEC 7816-6 data elements; stored asungrouped data elements encoded using IS/O/IEC Abstract Syntax Notation(ASN.1) as in IS/O/IEC 8824 and 8825; and/or other proprietarytechniques that may include fractal compression methods, imagecompression methods, etc.

In one exemplary embodiment, the ability to store a wide variety ofinformation in different formats is facilitated by storing theinformation as a BLOB. Thus, any binary information can be stored in astorage space associated with a data set. As discussed above, the binaryinformation may be stored on the financial transaction instrument orexternal to but affiliated with the financial transaction instrument.The BLOB method may store data sets as ungrouped data elements formattedas a block of binary via a fixed memory offset using either fixedstorage allocation, circular queue techniques, or best practices withrespect to memory management (e.g., paged memory, least recently used,etc.). By using BLOB methods, the ability to store various data setsthat have different formats facilitates the storage of data associatedwith the financial transaction instrument by multiple and unrelatedowners of the data sets. For example, a first data set which may bestored may be provided by a first party, a second data set which may bestored may be provided by an unrelated second party, and yet a thirddata set which may be stored, may be provided by an third partyunrelated to the first and second party. Each of these three exemplarydata sets may contain different information that is stored usingdifferent data storage formats and/or techniques. Further, each data setmay contain subsets of data that also may be distinct from othersubsets.

As stated above, in various embodiments, the data can be stored withoutregard to a common format. However, in one exemplary embodiment, thedata set (e.g., BLOB) may be annotated in a standard manner whenprovided for manipulating the data onto the financial transactioninstrument. The annotation may comprise a short header, trailer, orother appropriate indicator related to each data set that is configuredto convey information useful in managing the various data sets. Forexample, the annotation may be called a “condition header”, “header”,“trailer”, or “status”, herein, and may comprise an indication of thestatus of the data set or may include an identifier correlated to aspecific issuer or owner of the data. In one example, the first threebytes of each data set BLOB may be configured or configurable toindicate the status of that particular data set; e.g., LOADED,INITIALIZED, READY, BLOCKED, REMOVABLE, or DELETED. Subsequent bytes ofdata may be used to indicate for example, the identity of the issuer,user, transaction/membership account identifier or the like. Each ofthese condition annotations are further discussed herein.

The data set annotation may also be used for other types of statusinformation as well as various other purposes. For example, the data setannotation may include security information establishing access levels.The access levels may, for example, be configured to permit only certainindividuals, levels of employees, companies, or other entities to accessdata sets, or to permit access to specific data sets based on thetransaction, merchant, issuer, user or the like. Furthermore, thesecurity information may restrict/permit only certain actions such asaccessing, modifying, and/or deleting data sets. In one example, thedata set annotation indicates that only the data set owner or the userare permitted to delete a data set, various identified users may bepermitted to access the data set for reading, and others are altogetherexcluded from accessing the data set. However, other access restrictionparameters may also be used allowing various entities to access a dataset with various permission levels as appropriate.

The data, including the header or trailer may be received by a standalone interaction device configured to add, delete, modify, or augmentthe data in accordance with the header or trailer. As such, in oneembodiment, the header or trailer is not stored on the transactiondevice along with the associated issuer-owned data but instead theappropriate action may be taken by providing to the transactioninstrument user at the stand alone device, the appropriate option forthe action to be taken. The system may contemplate a data storagearrangement wherein the header or trailer, or header or trailer history,of the data is stored on the transaction instrument in relation to theappropriate data.

One skilled in the art will also appreciate that, for security reasons,any databases, systems, devices, servers or other components of system100 may consist of any combination thereof at a single location or atmultiple locations, wherein each database or system includes any ofvarious suitable security features, such as firewalls, access codes,encryption, decryption, compression, decompression, and/or the like.

The system and method may be described herein in terms of functionalblock components, screen shots, optional selections and variousprocessing steps. It should be appreciated that such functional blocksmay be realized by any number of hardware and/or software componentsconfigured to perform the specified functions. For example, the system100 may employ various integrated circuit components, e.g., memoryelements, processing elements, logic elements, look-up tables, and thelike, which may carry out a variety of functions under the control ofone or more microprocessors or other control devices. Similarly, thesoftware elements of the system may be implemented with any programmingor scripting language such as C, C++, C#, Java, JavaScript, VBScript,Macromedia Cold Fusion, COBOL, Microsoft Active Server Pages, assembly,PERL, PHP, awk, Python, Visual Basic, SQL Stored Procedures, PL/SQL, anyUNIX shell script, and extensible markup language (XML) with the variousalgorithms being implemented with any combination of data structures,objects, processes, routines or other programming elements. Further, itshould be noted that the system may employ any number of conventionaltechniques for data transmission, signaling, data processing, networkcontrol, and the like. Still further, the system could be used to detector prevent security issues with a client-side scripting language, suchas JavaScript, VBScript or the like. For a basic introduction ofcryptography and network security, see any of the following references:(1) “Applied Cryptography: Protocols, Algorithms, And Source Code In C,”by Bruce Schneier, published by John Wiley & Sons (second edition,1995); (2) “Java Cryptography” by Jonathan Knudson, published byO'Reilly & Associates (1998); (3) “Cryptography & Network Security:Principles & Practice” by William Stallings, published by Prentice Hall;all of which are hereby incorporated by reference.

These software elements may be loaded onto a general purpose computer,special purpose computer, or other programmable data processingapparatus to produce a machine, such that the instructions that executeon the computer or other programmable data processing apparatus createmeans for implementing the functions specified in the flowchart block orblocks. These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function specified in the flowchart block or blocks.The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer-implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

Accordingly, functional blocks of the block diagrams and flowchartillustrations support combinations of means for performing the specifiedfunctions, combinations of steps for performing the specified functions,and program instruction means for performing the specified functions. Itwill also be understood that each functional block of the block diagramsand flowchart illustrations, and combinations of functional blocks inthe block diagrams and flowchart illustrations, can be implemented byeither special purpose hardware-based computer systems which perform thespecified functions or steps, or suitable combinations of specialpurpose hardware and computer instructions. Further, illustrations ofthe process flows and the descriptions thereof may make reference touser windows, web pages, web sites, web forms, prompts, etc.Practitioners will appreciate that the illustrated steps describedherein may comprise in any number of configurations including the use ofwindows, web pages, web forms, popup windows, prompts and/or the like.It should be further appreciated that the multiple steps as illustratedand described may be combined into single web pages and/or windows buthave been expanded for the sake of simplicity. In other cases, stepsillustrated and described as single process steps may be separated intomultiple web pages and/or windows but have been combined for simplicity.

Practitioners will appreciate that there are a number of methods fordisplaying data within a browser-based document. Data may be representedas standard text or within a fixed list, scrollable list, drop-downlist, editable text field, fixed text field, pop-up window, and/or thelike. Likewise, there are a number of methods available for modifyingdata in a web page such as, for example, free text entry using akeyboard, selection of menu items, check boxes, option boxes, and/or thelike.

Referring now to the figures, the block system diagrams and process flowdiagrams represent mere embodiments of the invention and are notintended to limit the scope of the invention as described herein. Forexample, the steps recited in FIGS. 2-5 may be executed in any order andare not limited to the order presented. It will be appreciated that thefollowing description makes appropriate references not only to the stepsdepicted in FIGS. 2-5, but also to the various system components asdescribed above with reference to FIG. 1.

UG 165 includes software modules that execute logic and processes forimporting data from external systems or data feeds, rationalizing thedata, parsing the data into IBOs that are stored in object database UGOD190, enhancing the data objects by discovering and storing significantcontext using artificial intelligence techniques, storing system usersas IBOs in the system and/or forming an understating of the user'scontext. UG 165 treats each IBO the same and gives each IBO the abilityto be self-aware and self-learning. This approach allows the UG 165 togenerate answers regarding the data and the business implications of thedata even before specific questions are asked. UG 165 contextualizesanswers via relevant significance to yield useful result sets, which arethen prioritized by the user against other result sets obtained acrossall dimensions and all interested parties. The user and/or the object'simbedded perception drives contextualization.

FIG. 3 is a flowchart illustrating a process overview of one embodimentof the invention. UG 165 overlays intelligent business objects, known asS/Os, with a neural network program within an expert system frameworkutilizing predictors and models optimized with fitness functions (Step301). UG 165 creates and processes intelligent business objects (IBOs)that are stored in UGOD 190 as S/Os (step 305). S/Os represent both thedata and the users in the system. UG 165 determines the contextualperspective of all S/Os (step 306). In one embodiment, S/Os are bothdynamic and intelligent and they constantly look for ways to self-learnand become self-aware. S/Os increase their value by interacting (e.g.,continuously interacting) with other S/Os in UG 165.

As the S/Os interact, UG 165 creates (e.g., continuously creates)hierarchies and relationships among the S/Os (step 310). As part of thisinteraction, UG 165 also generates significance measurements usingprobabilistic models (step 315). Through this interaction, the S/Osdiscern significant context and learn from that context. UG 165 overlaysthe contextual perspective of the user S/Os onto the universe of thediscerned data (Step 320). Through this process of continual learningand contextual alignment, the experiences of an organization arecaptured, yielding probabilistic result sets that are presented to auser in the user's unique context (step 325). The results themselves arein a constant state of refinement based on the user's context andconstant new transactions (step 330). Furthermore, the user's context isalso refined (step 335).

The UG 165 imparts understanding using dynamic artificial intelligence(“AI”), expert systems techniques, transaction processing, statisticalmethods (e.g. Bayesian networks), business intelligence techniquesand/or database technology. In UG 165, a user does not pose repetitivequestions since each object in UGOD 190 understands its own context withrelation to all (or a certain subset of) other objects. Hence, the userS/O proxy (“user's S/O”) understands the user's requirements that willpromote understanding within the limits of the user's context. The morerefined the context, the more the result sets delivered are aligned withuser understanding. In this way, UG 165 develops an understanding of thedistance between where the user is and the desired outcome, i.e. UG 165anticipates what the user really wants to know.

For instance, in one embodiment, the system 100 is implemented to managethe supply chain and materials management for multiple hospitals. Thehospital data and user community is processed by UG 165. The artificialintelligence components of UG 165 cause the S/Os, both user and data, tointeract with other S/Os to create memories. These memories are queriedand processed via web services for use by user 105. One set of serversconstantly processes transactional data and another set of computingresources run the artificial intelligence functions that discernquestions and answers. Answers are staged based on the user's contextand relevance feedback is received directly from the user and indirectlyby system events (both of which are, for example, encapsulated in ICOsassociated with the user's S/O).

IBO creator 167 creates a proxy S/O to represent user 105 (“user'sS/O”). The user's S/O is the user's facsimile, i.e., a systeminterpretation of a user's contextual perspective. Context profiler 168defines context (what and why something is important together with itsrelationships) of all IBOs including human users. To enableunderstanding of itself and of the other S/O's in the system, every S/Ois given a context at initialization. Each S/O is a self-aware softwareentity which has context imparted to it via an ICO, i.e. context ismaintained for each S/O within an ICO which holds all (or any portionof) relative and relevant context information. ICOs are owned by theS/Os and define the applied contextual parameters such as relationships,object energy values, probabilities, planes of significance (cost,revenue, patient outcome) probabilities and/or other aspects of context.

Context for the user's S/O comes from external inputs such as the user's105 organization and job function, Myers-Briggs personality profiles,user 105 search history and user 105 feedback. Data S/Os (which can bethought of as the non-human “users” of UG 165) are imparted contextthrough cost, revenue and outcome data. Artificial intelligencetechniques assist an ICO to discern more knowledge through datarationalization, whereby the ICO interacts with other S/Os and ICOs. Bymonitoring user interaction, the UG 165 updates the user S/Os contextand the associated perception. The change in context is communicated(e.g., immediately communicated) to all other S/Os, which in turn,create new memories.

Capturing industry experience and deriving practical user targetedunderstanding, starts with identifying useful inputs. In one embodiment,the UG 165 is implemented in the healthcare industry and its inputsinclude the health level seven (HL7) schemas, health care datatransactions, and/or user input interfaces to seek/obtain results andset contexts. UG 165 analyzes these concepts of use to build its conceptspace. HL7 is an American National Standards Institute (ANSI) accreditedstandards developing organization (see, www.hl7.org). In one embodiment,IBOs are defined from the HL7 schema, which is mapped to UG 165functionality.

In UG 165, raw data is translated into IBOs and transactional data isfed to DD 167 from purchase order data, invoices, payor contractrecords, billing records, and other industry related data stored in EDMS150. DD 167 rationalizes data entry into the system and is fundamentalin the creation of IBOs. Transactions are imported and substantiated,self-evolved and related to their associated S/Os. As a data feed isprocessed, the IBO creator 167 checks to see if an S/O already existsfor the item being processed. If it does not, then IBO Creator 167creates a new S/O. In one embodiment, the data feed schema is viewed ina data rationalizer user interface that assists a user in translatingthe data feed schema by, for example, identifying the specific entitieswithin the data feed schema. Essentially, this data rationalizer assistsin defining S/O attributes and associated IBOs.

In one embodiment, transactional data items related to an S/Orepresenting an item of inventory in a hospital's material managementsystem (e.g. a syringe or an artificial hip) are processed by DD 167.For example, an artificial hip (i.e. the S/O being processed) is relatedto data items such as the cost of the item, the quantity associated withthe transaction and unit of measure and these related data values arestored in an IEO. Furthermore, in this example, the associated hospitaldata item is also created as an S/O in UG 165 and an IRO is createdbetween the artificial hip and the new hospital S/O.

S/Os in UG 165 are intrinsically programmed to evolve and accomplish twoprimary directives, “Who am I” and “How can I serve?” The evolution ofan S/O is driven by the artificial intelligence aspects of the UG 165based upon fundamental concepts. For instance, an SO comes into beingwith its core physical characteristics which exist no matter how it isperceived by other objects in UG 165 or external systems. Thus, anobject may initially only be associated with a single name or itemnumber. However, over time the object is used for many purposes andassociated with different costs and risks. Hence, the object may bereferred to differently depending upon the perspective of the user orthe purpose for which the object is being used. UG 165 builds objectsfrom their physical being outward where the contextual weightedassociations are created with underlying belief systems driven by cost,revenue and outcome.

One example that illustrates these functions and features is a bandageand its use in a healthcare facility. The bandage is initially definedby its physical characteristics—size, shape, color, composition etc. Thebandage is related to other items or objects in the healthcare facilityand the bandage may be referred to differently depending on the functionit is serving or the perspective of the user. Hence, an S/O thatrepresents the bandage develops a sense of “Who am I” by developingconnections to objects, procedures, costs and by developing aliases(e.g. synonyms) representing “who it is” based on varying contexts.Similarly, as the many uses of the bandage are recorded by UG 165 andother S/Os interact with it, the S/O representing the bandage in UG 165gets a sense of “How do I Serve.” Each of these elements concerning “Whoam I” and “How do I Serve” has context based on who uses it, when it isused, how it is used, what is used with it, where it is used and theirlevel of interest in the primary business driver's of cost, revenue andoutcome. Thus, based on AI analysis on all planes for all dimensionsfrom the subject's perspective a bandage develops its definitions of“Who am I” and “How do I Serve.” UG 165 uses the intelligence capturedin these intelligent objects to provide contextually significantinformation to users. UG 165 users receive contextual information with aProbability of Fitness answering “Who the Bandage is” and “How itserves.” Results are generated depending on the contextual overlay ofthe user (e.g. whether the user is a nurse, a floor manager, anaccountant or the hospital's chief operating officer) and users arepresented information (e.g. name, uses, cost, outcome) in appropriatecontext and ordered based on an underlying belief system's expression ofan interest in cost, revenue and outcome.

In one embodiment, to accomplish this evolution, input data stored inEDMS 150 is processed by DD 167 using a combination of probabilisticcalculation and S/O relationships to discern who and what the S/O objectis. As S/Os enter into UG 165 all links are established based on theS/Os internal belief system. In one embodiment in the healthcareindustry, factors such as cost, revenue and patient outcome may be usedto measure the significance of the data. These core elements are usedthroughout UG 165 and many are evolved using AI techniques. In oneembodiment, the Ontology-Based Health Care Data Integration andNormalization Tools Protégé-Owl™ is used to enable the evolution of theIBOs using AI. In one embodiment, custom AI techniques and softwaremodules are used to interpret schemas and align data items based ontransactional use gathered from hundreds of hospitals, businesscatalogs, business transactions and business reports that reveal theirown context and use.

Data Awareness takes data from its raw form (or almost raw) and evolvesit into contextualized memory. An evolving S/O can be thought of as asubject where every other S/O is an object. By being a subject (i.e. anobserver), an S/O may compute its relationships relative to the otherobjects (i.e. all other S/O's in the system), clean up its descriptionand attributes, accrue spatial values, associate to the planes ofawareness and understand its basic function and how it is contextuallysignificant. FIG. 6 presents an awareness ladder illustrating how dataawareness is applied in one embodiment. As the data becomes moreintelligent and memories are created with the context controlling theresult sets, the impact is like that of having a consultant “fitted” tothe user's awareness level. For example, at Level 1 users needconsultants to make decisions. Similarly, users that know how to makedecisions typically need help in validating the decisions, and so on, upthe chain until it reaches a level of complete understanding whereby theuser utilizes little or no external support because the data andcorresponding action steps are self-evident.

New S/Os trigger DDs 167 bilateral data discernment process in responseto each new S/O entering UG 165 creating a ripple effect with allexisting S/O. The subject object bi-directional relationship yieldsperspective. The subject object relationship is applied to all S/Os andeach object “looks back” based on its significance to the defined S/Osubject. The subject overlays its context on its related (subjective)objects and the object shows the subject what the subject expects to seerelative to the context used in the “looking” process. All S/Osrelevance is based on the perspective of the subject and the alignmentof the objects' context therein. Significance is arbitrary and changesdepending on who/what is the subject. For instance, a person's top 10music songs might not be the same as another person's, even though theymay want to align with the “peer group”. This is a departure fromexisting system architecture which seeks to impose a fixed perspectivein the form of relationships and categories.

Discernment is based on a probabilistic assessment of the contextualsignificance of the data. DD 167 uses probabilities and normaldistribution curves to identify those values that have the mostprobability of contextual significance which is known as probability offitness or “POF.” POF is determined by the contextual perspective of theuser. In UG 165, significance revolves around cost, revenue and outcome(CRO). The more in tune UG 165 is with each user's CRO and each object'sCRO, the better the ability to report and project results, especiallywhen the Belief System reflects the contextual significance of CRO atthe field level.

A POF has two primary purposes. In particular, a POF is used to definewhat has taken place and to define the threshold of “relativesignificance” for some S/O event. Positive discernment occurs when thePOF of the object is greater than the POF requirement defined by theinterrogating subject. In one embodiment, when positive discernmentoccurs, the associated property values of the object are merged withthose of the subject. In one embodiment, the POF value is systemdetermined based on statistical analysis and/or artificial intelligence,and in the absence of sufficient data to support machine learning, thePOF is established by a domain expert.

In UG 165, an S/O's BS helps the S/O define “Who it is” in relation tothe other S/Os in the system. While traditional systems typically definedata based upon a schema, UG 165 instantiates an S/O within a beliefsystem that gives the S/O some definition, while also allowing the S/Oto self-define based upon interaction with other S/Os. During thisprocess of self-defining, the S/O traverses the different operatinglevels of its belief system as defined by the table below. In oneembodiment, seven operating levels exist within a class or speciesBelief System (BS):

Level Type Characteristic 1 Quarantine No History. Subject creates itsown map within the universe of data. The map is dynamic. 2 GeneralPopulation S/Os ordered by their own inherent context based upon BS. 3Differentiation of description Typically description. Introduce requiredto validate primary user context and evaluate based properties upon POF.4 Significance of one of the Cost, revenue, outcome. dynamic values 5Significance of two of the dynamic values 6 Significance of all three ofthe dynamic values 7 Autonomous self aware agent Not limited byindividual BS. Inherited context comprised of multiple schemaproperties.

The BS is designed to progress a S/O to a level for serving the needs ofanother S/O, including user S/Os. The more complex the S/O, (typicallyassociated with multiple dynamic values), the higher the awareness levelof the S/O BS, and the more complex is its associated schema andrelationships.

In one embodiment, Level-1 of the BS operates according to the followingprocess:

-   -   All S/Os are instantiated into Level-1 (quarantine) having a        minimum of an identifier (“ID”) and at least one other property.    -   Items are instantiated with full properties as directed by the        level-1 BS.    -   Properties pertaining to an S/O contained in quarantine can be        used to validate other S/O properties, but cannot be inherited        until the quarantined S/O is released from quarantine.    -   S/Os in quarantine participate in the originating source        processing and output results.    -   A source other than the originating source for any given ID in        quarantine does not have access to a quarantined ID.    -   All IDs require 3 independent sources to validate their        authenticity before being released from Level-1 quarantine.

The following example illustrates the above rules: A data source fromone hospital, “Hospital A,” provides purchase order data to UG 165. AnID for an x-ray machine is imported as part of the data feed and an S/Ois created and assigned to Level-1 of the BS. The S/O for the x-raymachine can be part of the analysis and results for Hospital A, but notfor other hospitals. Furthermore, other S/Os from the same data feed asthe x-ray machine's S/O are available for interaction (e.g. buildingrelationships and/or inheriting properties), but S/O's originating fromother data feeds are not available for interaction.

In one embodiment, Level-1 of the BS operates according to the processillustrated in FIG. 4. All S/Os are instantiated into Level-1(quarantine) having a minimum of an identifier (“ID”) and at least oneother property (step 405). Items are instantiated with full propertiesas directed by the Level-1 BS (step 410). Properties pertaining to anS/O contained in quarantine can be used to validate other S/Oproperties, but in one embodiment, cannot be inherited until thequarantined S/O is released from quarantine (step 415). S/Os inquarantine participate in the originating source processing and outputresults, but a source other than the originating source for any given IDin quarantine does not have access to a quarantined ID (step 420). Inone embodiment, all IDs include 3 independent sources to validate theirauthenticity before being released from Level-1 quarantine (step 425).

The following example illustrates the above rules: A data source fromone hospital, “Hospital A,” provides purchase order data to UG 165. AnID for an x-ray machine is imported as part of the data feed and an S/Ois created and assigned to Level-1 of the BS. The S/O for the x-raymachine can be part of the analysis and results for Hospital A, but notfor other hospitals. Furthermore, other S/Os from the same data feed asthe x-ray machine's SO are available for interaction (e.g. buildingrelationships and/or inheriting properties), but S/Os originating fromother data feeds may not be available for interaction.

Following the Level-1 instantiation process, all S/Os can inheritproperties from other S/Os. FIG. 5 illustrates S/O interaction within UG165. One S/O includes linkage with another S/O (step 505). All S/O“objects” can only become “subjects” in order to satisfy their primaldirectives of determining “Who am I” and “How can I serve.” Therefore,an S/O object converts to a subject by another S/O and its propertyvalues are insufficient to satisfy: its own BS or another S/O request,including a request from the user's proxy S/O. One S/O interrogatesanother S/O (step 510) and inherits desired properties (step 515). Theinterrogated S/O becomes the “subject” (step 520) and it progressesthrough the species (class) BS hierarchy based on interactions fromother S/Os and the need to satisfy property values requests (step 525).Items progress within the belief system to the level sufficient toacquire the information for responding to the questioner (representingthe process of dynamic linkage of S/Os).

The subject inherits properties of the interrogating object and gainspartial visibility and understanding (step 530). UG 165 determines ifthere is a source conflict (i.e. properties that should be equal are notequal). If there is a source conflict, UG 165 validates properties fromthe parent (step 536), identifies the change value (step 537), createsan alias for the legacy value (step 538) and creates an associationbetween the legacy value and the new value (i.e. the conflicting value)(step 539). If there is no source conflict, then UG 165 determines ifthere is positive discernment (step 540). If there is positivediscernment, the property values of the subject and the object aremerged (step 560). If there is no positive discernment, then UB 165checks the BS operating level (step 550). If the BS operating level is 6or greater, the subject S/O is re-instantiated as an autonomous agent(e.g. moves to BS level-7) (step 555) and UG 165 determines if there ispositive discernment (step 540). If the BS operating level is less than6, then the subject S/O moves to the next higher BS operating level(step 525) and the process repeats. This process represents amodification of the original item schema to a new hybrid schema in whichthe S/O subject has gained partial visibility and understanding of theinterrogating S/O object, as directed by the operating belief system.

An example of this process is a surgical procedure S/O that has aparticular item ID for a scalpel contained in one of its attributes. Inthis case, the procedure S/O BS requires a linkage with the scalpel,i.e. the S/O associated with the Item ID. This precedence merger of BSscauses property inheritance. The procedure S/O interrogates the Item S/Oand inherits the desired Item properties. This action also causes theItem to activate as a subject and move to BS Level-4. At this level, theItem S/O inherits additional properties normally associated with aprocedure ID. However, the Item's view of the procedure is limited bythe operating constraints of the Level-4 BS. This process continuesuntil the scalpel S/O has reached level 7 at which point the scalpel S/Ois re-instantiated as an independent autonomous agent with a dynamic BSbased on external contextual overlays. The interrogating S/O is alwaysthe driver and has its needs answered first.

Essential essence is defined as the minimum number of properties foruniversal identification of itself. In other words, essential essence isthe moment in time definition of an S/O's “Who am I” as it representsthe minimum critical attributes that defines a thing, whether that thingis an item, procedure, doctor, etc. It is the accumulation of thesecritical/essential attributes that defines when something is“universally understood.” For example, item number “DRG209” isuniversally known as a hip replacement procedure; however, item number172937-2 can be represented by multiple manufacturers. Differentiationin the later example includes additional properties to ensure correctidentification (e.g. description and/or manufacturer ID).

The energy fields are assessed with the subjects' dimensionalsignificance to see if a probability significance threshold has beenreached. The energy band is codified and a perspective window ofsignificance is discerned. Hence, if the Chief Financial Officer had aninterest in items whose cost increased by more than 20%, it would bedeemed significant and processed accordingly. The context can look foreach or any combination of significance dimensions and the probabilityof significance selects and aligns the results. Context drives theselection process which can be carried out on demand or cyclically. Theprocess of the subject and the object looking back allows the dataitself to resolve value, relationship and schema inconsistencies byapplying neural network and forecasting techniques to allow the data todefine itself (through experience and evolution) and recast itselfaccording to the probabilistic significance of multiple and/or dynamiccontexts. AI tools employed by UG 165 include Bayesian Reasoning,Bayesian Prediction, Bayesian Classifier, Back Propagation, DecisionTrees, K-Means, Nearest Neighbor and other classic algorithms. For moreinformation on AI techniques see “Artificial Intelligence a ModernApproach” by Stuart Russel and Peter Norvig; “Machine Learning” by TomMitchell; and “Pattern Classification and Scene Analysis” by RichardDuda and Peter Hart, each of which are hereby incorporated by reference.

Within the UG 165, each S/O (e.g. purchase order line item) has as thesame or similar significance as any other S/O. For example, in anembodiment, a titanium hip has as much potential capability within theUG as a plastic screw. Each has its own S/O that has the power todiscern all of its potential relationships. For example, an SO thatrepresents a disease related grouping (DRG) (which is an inpatientprocedure code) would have the ability to discover and develop itsrelationship with DRG's in other hospitals and/or with outpatientprocedure codes (e.g. procedure codes represented in current procedureterminology (CPT)). As each S/O and group of S/Os has a CRO significancevalue, the presentation of S/Os is prioritized based on significancethat has been defined in the user's proxy context. In one embodiment,for a user 105 that is a materials manager for a hospital, a syringe byitself has only a minimal cost CRO significance. However, all of thecosts of all of the items for the manufacturer of the syringe will havea far more significant CRO value and would be likely of interest, so theS/O representing the materials manager would have a context associatedwith it that reflects that the CRO value is significant. However, thesame CRO value would not be significant to a user 105 who is, forexample, a chief financial officer.

UG 165 encapsulates “snap-shots” of data, relationships and context asmemories. Memories are created by memory creator 169 when contextuallyappropriate. In one embodiment of UG 165, context is unique for everyS/O. The combination of an S/O's energy value (stored in an IEO),context (stored in an ICO) and time, produce “thoughts” (i.e.significant events) which are captured as potential memories by memorycreator 169. Memory creator 169 measures the significance of thepotential memory and those that reach a threshold of significance arestored as IMOs together with their spatial sequence in time.

For example, when an S/O interacts with another object, this subjective“experience” is determined relevant based on the operating belief systemof the viewing S/O in determination of the S/O's primary directives(i.e. “who am I” and “how do I serve”). The SO context drives thecreation of memories which are in effect a picture matching the desiredprobability of contextually significant values, relationships, CRO andtime frames. If the encounter is considered significant to the viewingS/O then the inherent values derived from the objective S/O, IEO and IBOare stored as an IMO. IMOs retain all (or a portion of) associatedvalues, attributes, temporal, contextual and special. Each memory has aspatial time dimension attached to it known as an Intelligent SpatialObject (ISO). Thus, memories contain the energy values, but also thecontext information including relationships and planes of significancethat were involved in creating the memory. These memories are capturedand immediately available for contextual overlays. In one embodiment,the memory creation process is driven by artificial intelligencetechniques and is modeled after the process human beings use to memorizesignificant thoughts. Appropriately aligned “thoughts” are stored ascontextual memories.

The memory creator 169 is triggered by user 105 input to creatememories. Once a user signs on, if this is the first time, an S/O iscreated as a facsimile of the user 105 in the UG 165. The user 105's S/Ois assigned an initial operating belief system. User's 105 S/O interactswith the existing S/O network to create a “memory pool” that is uniqueto user 105.

Relationships are established in UG 165 by relationship generator 171.In UG 165, entities actualize their impact based on the probability ofimpacting everything else through their relationships. Relationships areboth defined by DD 167 via user input by associating input data schemafields to related S/Os, as well as derived via source, genealogy,precedent and other information via relationship generator 171. Forexample, in one embodiment, a PO Line Item is associated to a vendor,and a hospital. If the associated SOs do not exist, the AI RelationshipGenerator creates the SOs and subsequently associates the source S/O tothe, now new, target S/Os.

Relationship generator 171 creates three types of relationships: 1)Independent—Explicit relationship related independently to itself; 2)Dependent—Implicit relationship, e.g. parent (item's relationship itsmanufacturer) or sibling (other items made by the same manufacturer); 3)Interdependent—Inter-related objects, e.g. competitive manufacturer'sitem. Relationship Generator 171 creates these different types ofrelationships and keeps track of changes in relationships. Relationshipsare used extensively within UG 165 to drive context and memories.

UG 165 determines the answers to questions before they are asked.Various components of UG 165 process raw data, develop relationshipsamong the data, evolve the data based upon its significance and createcontextualized memories of data conditions that maximize POF functions.In addition to determining potential result sets based upon actualevents or data values, what-if analyzer 172 forecasts alternativeoutcomes together with the outcome's respective potentiality. What-ifanalyzer 172 creates (e.g., constantly creates) memories by askingpertinent questions on each plane of awareness (cost, revenue, patientoutcome) and at each “node” of business content. Only significantresults from the what-if analyzer are stored as memories (e.g. bycreating an IMO). Hence, UG 165 is more like a significantcontextualized memory search engine, than a conventional system. Thatis, for the most significant S/Os, what-if analyzer 172 duplicates the“what-if” thought process that would typically surround the mostsignificant S/Os, i.e. How does an organization improve performance tomaximize or mitigate the CRO results set? What-if analyzer 172 usesadvanced probabilistic reasoning to create comparative understanding ofthe data in the context of the business.

What-if analyzer 172 first creates a bell curve of e-fields andtruncates the two tails of the bell curve based upon significance plus avariance. What-if analyzer 172 then re-assembles them by re-running thewhat-if scenario in the context of that curve to form a normaldistribution looking at the centers representing a maximum productivityof effort. Memory creator 169 creates understanding surroundingempirical information by staging active memories based on contextualsignificance and creating understanding surrounding these memories. UG165 identifies the most significant S/Os (based on the existing usercontext as defined in the belief system) and what-if analyzer 172creates outputs that provide additional understanding surrounding themost significant S/Os that are being presented.

In one embodiment, the corrective action analyzer 175 calculatescorrective action steps to substantially meet original targetperformance objectives. In one embodiment, corrective action analyzer175 projects probability thresholds of predictive outcomes based onalternative scenarios. The result sets may also be enhanced by trendanalyzer 173 which predicts change in potential outcomes based ondynamic status of user versus selected peer group.

Context search engine 176 provides contextual search capabilities thatcan be integrated into predictive models and used to help defineidentification and function. The context search engine 176 can alsoserve as an external data gathering/integration tool by retrieving datafrom an external search engine for incorporation into UG 165. Forexample, context search engine 176 may be used to locate and integratepatient outcome data. Throughout the search and results examinationprocess, the active input by user 105 has the potential to change andreorder the user's 105 contextual significance CRO result set andcorresponding S/Os. UG 165 uses the results formatter 174 as a reportgenerator that presents information in formats that promote easy userunderstanding, e.g. summary or chart. The results are prioritized bothexplicitly and by use analysis. In one embodiment, UG 165 provides acontextual path trace function that allows user 105 to view the analysisperformed to develop the significance of the data being presented. Inone embodiment, results are presented in a graphic user interfacedashboard format that provides user screens for soliciting user contextdata, performing user driven what-if analysis, viewing results, andconfiguring custom reports.

While the steps outlined above represent a specific embodiment of theinvention, practitioners will appreciate that there are any number ofcomputing algorithms and user interfaces that may be applied to createsimilar results. The steps are presented for the sake of explanationonly and are not intended to limit the scope of the invention in anyway. Benefits, other advantages, and solutions to problems have beendescribed herein with regard to specific embodiments. However, thebenefits, advantages, solutions to problems, and any elements that maycause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as critical, required, or essentialfeatures or elements of the invention. The scope of the invention isaccordingly to be limited by nothing other than the appended claims, inwhich reference to an element in the singular is not intended to mean“one and only one” unless explicitly so stated, but rather “one ormore.” Moreover, where a phrase similar to ‘at least one of A, B, and C’is used in the claims, it is intended that the phrase be interpreted tomean that A alone may be present in an embodiment, B alone may bepresent in an embodiment, C alone may be present in an embodiment, orthat any combination of the elements A, B and C may be present in asingle embodiment; for example, A and B, A and C, B and C, or A and Band C.

Although the invention has been described as a method, it iscontemplated that it may be embodied as computer program instructions ona tangible computer-readable carrier, such as a magnetic or opticalmemory or a magnetic or optical disk. All structural, chemical, andfunctional equivalents to the elements of the above-described exemplaryembodiments that are known to those of ordinary skill in the art areexpressly incorporated herein by reference and are intended to beencompassed by the present claims. Moreover, it is not necessary for adevice or method to address each and every problem sought to be solvedby the present invention, for it to be encompassed by the presentclaims. Furthermore, no element, component, or method step in thepresent disclosure is intended to be dedicated to the public regardlessof whether the element, component, or method step is explicitly recitedin the claims. No claim element herein is to be construed under theprovisions of 35 U.S.C. 112, sixth paragraph, unless the element isexpressly recited using the phrase “means for.” As used herein, theterms “comprises”, “comprising”, or any other variation thereof, areintended to cover a non-exclusive inclusion, such that a process,method, article, or apparatus that comprises a list of elements does notinclude only those elements but may include other elements not expresslylisted or inherent to such process, method, article, or apparatus.

The invention claimed is:
 1. A contextual artificial intelligence (AI)system, comprising: an Intelligent Business Object (IBO) creatorconfigured to: create a user proxy intelligent physical object (IPO);create a first IPO which is configured to store static attributes of thedata element; create a first intelligent energy object (IEO) which isconfigured to store the dynamic attributes of the data element; createan intelligent context object (ICO) which is configured to store acontext of the data element; and, associate at least one of: the firstIPO, the first IEO or the first ICO with at least one of: the first IPO,the first IEO or the first ICO an AI context profiler configured tointerpret a user profile to create a user context and encapsulate theuser context in a user context intelligent context object (ICO), whereinthe user profile comprises at least one of a job function, a personalityprofile, user preference, a user history, a user action, or aMyers-Briggs personality profile; and an AI data rationalizer configuredto process input data and control data evolution.
 2. The system of claim1, a contextual AI processor, a tangible, non-transitory memoryconfigured to communicate with said processor, said tangible,non-transitory memory having instructions stored thereon that, inresponse to execution by said processor, cause said processor to executethe functions of the IBO, the AI context profiler and the AI datarationalizer.
 3. The system of claim 1, further comprising an AIrelationship generator configured to create a first intelligentrelationship object (IRO) which is configured to store a relationshipbetween at least two objects.
 4. The system of claim 3, wherein the AIrelationship generator is configured to create the first IRO in responseto at least one of an external event, user input, a data input, a changein value of an object or a change in the user proxy IPO.
 5. The systemof claim 3, wherein the first IRO relates at least one of data schemafields, significant values, event or time frames.
 6. The system of claim3, wherein the first IRO comprises at least one of a dependentrelationship, an independent relationship or an interdependentrelationship.
 7. The system of claim 1, further comprising an AI memorycreator configured to create a first intelligent memory object (IMO). 8.The system of claim 7, wherein the AI memory creator is furtherconfigured to create the first IMO in response to the significance levelexceeding a threshold level.
 9. The system of claim 8, wherein the firstIMO comprises at least one of a contextually significant value,contextual parameter data, user contextual data, an object, a change toan object, a relationship, a user input or a time dimension.
 10. Thesystem of claim 9, wherein the AI memory creator is further configuredto store the time dimension as a spatial time dimension in anIntelligent Spatial Object (ISO).
 11. The system of claim 1, wherein theAI data rationalizer is further configured to control data evolutionusing a belief system.
 12. The system of claim 11, wherein the beliefsystem comprises multiple operating levels.
 13. The system of claim 12,wherein the AI data rationalizer is configured to use the belief systemat least partially to determine a property inheritance decision.
 14. Thesystem of claim 13 wherein the property inheritance decision isconfigured to determine that a portion of the data properties of thefirst IPO are inherited from a second IPO.
 15. The system of claim 13,wherein the AI data rationalizer is further configured to determine thesignificance level of at least one of the first IPO, the first IEO orthe first ICO based upon the user proxy IPO.
 16. The system of claim 13,wherein the AI data rationalizer is further configured to determine thesignificance level based upon at least one of a cost, a risk or anoutcome.
 17. The system of claim 16, wherein the AI data rationalizer isfurther configured to apply at least one of probabilistic modeling orneural network techniques to determine the at least one of thesignificance level, the property inheritance decision, or the threshold.18. The system of claim 9, wherein the AI data rationalizer is furtherconfigured to use at least one of the first ICO or the user proxy ICO asinput into the probabilistic modeling.
 19. The system of claim 18,further comprising a plurality of intelligent business objects (IBOs)wherein an IBO comprises at least one of an IPO, an IEO, an ICO, anintelligent relationship object (IRO) or an intelligent memory object(IMO).
 20. The system of claim 19, wherein the plurality of ISOs arestored in an object database.
 21. The system of claim 19, wherein the AIdata rationalizer is configured to control the data evolution inresponse to at least one of a change in a first ISO, a user input, anexternal event or a continuous AI process.
 22. The system of claim 19,wherein each IPO in the plurality of IBOs are configured to continuouslyself-define properties of each IPO through a process of interrogatingother IPOs.
 23. The system of claim 19, wherein the first IPO isconfigured to act as an agent for a second IPO.
 24. The system of claim23, wherein the first IPO is configured to at least one of resolve adata inconsistency associated with the second IPO, cause a second IRO tobe created, verify the values of a second IEO associated with the secondIPO, resolve a source conflict, or create a new IMO.
 25. The system ofclaim 22, wherein the first ICO is configured to inherit context from asecond ICO.
 26. The system of claim 19, wherein the AI memory creator isconfigured to continuously monitor potential memories comprising atleast one of user activity, user input or the plurality of IBOs.
 27. Thesystem of claim 26, wherein the AI memory creator is further configuredto evaluate the potential memories based upon at least one of a costsignificance, a revenue significance or an outcome significanceassociated with the user ICO.
 28. The system of claim 27, wherein the AImemory creator is further configured to create IMOs to create a tailoredresult set tailored to user contextual significance.
 29. The system ofclaim 28, further comprising a what-if generator configured to forecastrelationships and data values to create what-if predictions, identifysignificant predictions, or trigger the AI memory creator to save thesignificant what-if predictions as what-if IMOs in the tailored resultset.
 30. The system of claim 29, further comprising a trend generatorconfigured to at least one of identify trends in data values and tocreate trend predictions, identify significant trend predictions, ortrigger the AI memory creator to save the significant trend predictionsas trend-IMOs in the tailored result set.
 31. The system of claim 30,further comprising a results formatter configured to format the tailoredresult set into a result format for presentation to the user.
 32. Thesystem of claim 1, wherein the data rationalizer is further configuredto import a plurality of external data feeds.
 33. The system of claim32, wherein the plurality of data feeds comprise at least one of supplychain or materials management data.
 34. The system of claim 32, whereinthe plurality of data feeds comprise healthcare transaction data. 35.The system of claim 33, wherein the plurality of data feeds relates tomultiple hospitals.
 36. The system of claim 32, wherein the plurality ofdata feeds are in industry specific format.
 37. The system of claim 36,wherein the industry specific format is the HL7 schema.
 38. Acomputer-implemented method, comprising: creating, by an contextualartificial intelligence (AI) computer, a user proxy intelligent physicalobject (IPO); creating, by the computer, a first IPO which is configuredto store static attributes of the data element; creating, by thecomputer, a first intelligent energy object (IEO) which is configured tostore the dynamic attributes of the data element; creating, by thecomputer, an intelligent context object (ICO) which is configured tostore a context of the data element; associating, by the computer, atleast one of: the first IPO, the first IEO or the first ICO with atleast one of: the first IPO, the first IEO or the first ICOinterpreting, by an AI context profiler executed by the computer, a userprofile to create a user context and encapsulate the user context in auser context intelligent context object (ICO), wherein the user profilecomprises at least one of a job function, a personality profile, userpreference, a user history, a user action, or a Myers-Briggs personalityprofile; and processing, by an AI data rationalizer executed by thecomputer, input data and control data evolution.
 39. A non-transitorycomputer readable medium having instructions stored thereon that, inresponse to execution by a contextual artificial intelligence computer,cause said financial account issuer computer to perform operationscomprising: creating, by an contextual artificial intelligence (AI)computer, a user proxy intelligent physical object (IPO); creating, bythe computer, a first IPO which is configured to store static attributesof the data element; creating, by the computer, a first intelligentenergy object (IEO) which is configured to store the dynamic attributesof the data element; creating, by the computer, an intelligent contextobject (ICO) which is configured to store a context of the data element;associating, by the computer, at least one of: the first IPO, the firstIEO or the first ICO with at least one of: the first IPO, the first IEOor the first ICO interpreting, by an AI context profiler executed by thecomputer, a user profile to create a user context and encapsulate theuser context in a user context intelligent context object (ICO), whereinthe user profile comprises at least one of a job function, a personalityprofile, user preference, a user history, a user action, or aMyers-Briggs personality profile; and processing, by an AI datarationalizer executed by the computer, input data and control dataevolution.